随机森林
决策树
水准点(测量)
特征(语言学)
计算机科学
树(集合论)
特征向量
集成学习
人工智能
进化算法
模式识别(心理学)
遗传程序设计
回归
机器学习
进化计算
数学
统计
地理
大地测量学
数学分析
哲学
语言学
作者
Hengzhe Zhang,Aimin Zhou,Hu Zhang
标识
DOI:10.1109/tevc.2021.3136667
摘要
Random forest (RF) is a type of ensemble-based machine learning method that has been applied to a variety of machine learning tasks in recent years. This article proposes an evolutionary approach to generate an oblique RF for regression problems. More specifically, our method induces an oblique RF by transforming the original feature space to a new feature space through the evolutionary feature construction method. To speed up the searching process, the proposed method evaluates each set of features based on a decision tree (DT) rather than an RF. In order to obtain an RF, we archive top-performing features and corresponding trees during the search. In this way, both the features and the forest can be constructed simultaneously in a single run. The proposed evolutionary forest is applied to 117 benchmark problems with different characteristics and compared with some state-of-the-art regression methods, including several variants of the RF and gradient boosted DTs (GBDTs). The experimental results suggest that the proposed method outperforms the existing RF and GBDT methods.
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